topics, sentiment classification generally deals with two classes(positive versus negative), a range of polarity(e.g, star ratings for movies), or a range in strength of opinion What are the most popular application areas for sentiment analysis? Why? Customer relationship management(CRM) and customer experience management are popular"voice of the customer(VOC)applications. Other application areas include"voice of the market(VOM)and"voice of the employee (VOe) What would be the expected benefits and beneficiaries of sentiment analysis in olitics? Opinions matter a great deal in politics. Because political discussions are dominated by quotes, sarcasm, and complex references to persons, organizations, and ideas, politics is one of the most difficult, and potentially fruitful, areas for sentiment analysis. By analyzing the sentiment on election forums, one may predict who is more likely to win or lose. Sentiment analysis can help understand what voters are thinking and can clarify a cand idate's position on issues Sentiment analysis can help political organizations, campaigns, and news analysts to better understand which issues and positions matter the most to voters. The technology was successfully applied by both parties to the 2008 and 2012 American presidential election campaigns 4. What are the main steps in carrying out sentiment analysis projects? The first step when performing sentiment analysis of a text document is called sentiment detection, during which text data is differentiated between fact and opinion(objective vs subjective). This is followed by negative-positive(N-P) polarity classification, where a subjective text item is classified on a bipolar range Following this comes target identification(identifying the person, product, event, etc. that the sentiment is about ) Finally come collection and aggregation, in which the overall sentiment for the document is calculated based on the calculations of sentiments of individual phrases and words from the first three 5. What are the two common methods for polarity identification? Explain Polarity identification can be done via a lexicon(as a reference library )or by using a collection of training documents and inductive machine learning algorithms. The lexicon approach uses a catalog of words, their synonyms, and their meanings, combined with numerical ratings indicating the position on the n P polarity associated with these words. In this way, affective, emotional, and attitud inal phrases can be classified according to their degree of positivity or negativity. By contrast, the training-document approach uses statistical analysis and machine learning algorithms, such as neural networks, clustering approaches Copyright C2018 Pearson Education, Inc.8 Copyright © 2018Pearson Education, Inc. topics, sentiment classification generally deals with two classes (positive versus negative), a range of polarity (e.g., star ratings for movies), or a range in strength of opinion. 2. What are the most popular application areas for sentiment analysis? Why? Customer relationship management (CRM) and customer experience management are popular “voice of the customer (VOC)” applications. Other application areas include “voice of the market (VOM)” and “voice of the employee (VOE).” 3. What would be the expected benefits and beneficiaries of sentiment analysis in politics? Opinions matter a great deal in politics. Because political discussions are dominated by quotes, sarcasm, and complex references to persons, organizations, and ideas, politics is one of the most difficult, and potentially fruitful, areas for sentiment analysis. By analyzing the sentiment on election forums, one may predict who is more likely to win or lose. Sentiment analysis can help understand what voters are thinking and can clarify a candidate’s position on issues. Sentiment analysis can help political organizations, campaigns, and news analysts to better understand which issues and positions matter the most to voters. The technology was successfully applied by both parties to the 2008 and 2012 American presidential election campaigns. 4. What are the main steps in carrying out sentiment analysis projects? The first step when performing sentiment analysis of a text document is called sentiment detection, during which text data is differentiated between fact and opinion (objective vs. subjective). This is followed by negative-positive (N-P) polarity classification, where a subjective text item is classified on a bipolar range. Following this comes target identification (identifying the person, product, event, etc. that the sentiment is about). Finally come collection and aggregation, in which the overall sentiment for the document is calculated based on the calculations of sentiments of individual phrases and words from the first three steps. 5. What are the two common methods for polarity identification? Explain. Polarity identification can be done via a lexicon (as a reference library) or by using a collection of training documents and inductive machine learning algorithms. The lexicon approach uses a catalog of words, their synonyms, and their meanings, combined with numerical ratings indicating the position on the N￾P polarity associated with these words. In this way, affective, emotional, and attitudinal phrases can be classified according to their degree of positivity or negativity. By contrast, the training-document approach uses statistical analysis and machine learning algorithms, such as neural networks, clustering approaches